Summary

This project seeks to improve on the Howard et al. (2020) methods used to estimate sport fish harvest, catches and releases of rockfish in Alaska waters. This is essentially a Bayesian version of the Howard methods that allows for more appropriate and defensible sharing of information between areas, handles missing data in a more appropriate manor, accurately propagates uncertainty throughout the estimation procedure and thus does not rely on the decision tree approach in the original Howard methods. Furthermore, the Bayesian approach should provide sport fish harvest, catch and mortality estimates back to 1978 when the SWHS was implemented. Harvest estimates should be mostly consistent with Howard estimates during contemporary times, but may differ based on more appropriate weighting of SWHS and logbook data, including estimating and correcting bias in the SWHS data. Furthermore, the Howard methods are wholly reliant on logbook release estimates and ignore the release estimates from the SWHS data (inferred from the catch and harvest estimates). Here we explore several models that attempt to balance all of the data in estimating releases.

Data

Harvest data was available for 22 commercial fishing management areas in Southcentral and Southeast Alaska. Areas with negligible rockfish harvest were pooled with adjacent areas for analysis. Specifically the Aleutian and Bering areas were pooled into an area labeled BSAI; the IBS and EKYT were pooled into an area labeled EKYKT; the Southeast, Southwest, SAKPEN and Chignik areas were pooled into an area labeled SOKO2PEN and the Westside and Mainland areas were pooled into an area labeled WKMA.

Stateside Harvest Survey (SWHS)

Statewide harvest survey estimates of rockfish catch and harvest are available for 28 years (1996-2023) for all users and for 13 years (2011-2023) for guided anglers (Figure 0). Additionally, there are estimates from 1977- 1995 that required some partitioning work to ascribe to current management units. Harvests in unknown areas were apportioned based on harvest proportions in 1996. Variance estimates are not available for pre-1996 data and as such, the maximum observed coefficient of variation (cv) in each commercial fisheries management unit was applied.

**Figure 0.**- Data sources for estimating rockfish harvests and releases in ADF&G commercial fisheries management units.

Figure 0.- Data sources for estimating rockfish harvests and releases in ADF&G commercial fisheries management units.


SWHS estimates are believed to be biased to some degree. These modelling efforts aim to estimate and correct for that bias with the assumption that logbook harvests are a census of guided harvests.

Rockfish release estimates are inferred from the difference between catch and harvest estimates.

Adam noted that the first 5 years (23 years counting the historical data) in the SWHS data set for PWSO seem unreasonable (close to zero and not corroborated with logbook estimates). Adam recommended setting these harvests to unknown, but current model development has included the data. Once a satisfactory model has been identified we will exam the effects of censoring the PWSO data.

Creel Surveys

NA

Guide Logbooks

Sport fishing guides were required to report their harvest of rockfish for 26 years (1998-2023). Reported harvest is also available by assemblage (pelagic vs. non-pelagic). Harvest of yelloweye rockfish were reported separately beginning in 2006.

Logbooks also record the number of rockfish released for the same categories. However, the reliability of the release data is somewhat questionable as reported releases are generally far lower than that estimated by the SWHS. As such several treatments of the data are considered.

Logbook versus SWHS estimates

Estimates of guided harvests and releases from the SWHS do not align with the census from charter logbooks. Logbook harvest reports are generally considered reliable and are used to assess the bias in SWHS reports. However, there is even greater disparity between release estimates in the two sources and it is debatable whether logbook releases should be treated as a census. The Howard et al. (2020) methods do treat the logbook release data as “true” and thus are considerably less than would be estimated from the SWHS data.

**Figure 1.**- SWHS harvest estimates from guided trips (Hhat) versus repoted harvests from charter logbooks (H_lb).

Figure 1.- SWHS harvest estimates from guided trips (Hhat) versus repoted harvests from charter logbooks (H_lb).


The Howard methods treat the logbook release data as a census and then use the ratio of guided:unguided releases in the SWHS to expand the logbook release estimates to generate total and unguided estimates.

To evaluate this discrepancy, several models were used to estimate releases in this exploration. One method (\(LB_{fit}\)) considers the logbook release data to be reliable and a second method (\(LB_{cens}\)) treats the logbook release data as estimates of the minimum released, thus giving more weight to SWHS release estimates. A third method (\(LB_{hyb}\)) is a hybrid approach that treats reported releases of yelloweye as reliable but total rockfish and pelagic rockfish releases as minimums. Model development to date has revealed a tension between the total and pelagic logbook releases and the yelloweye logbook releases.

Composition data

Harvest sampling data exists from Gulf of Alaska areas since 1996 and from Southeast Alaska areas since 2006. Port sampling data is comprised of the number of total rockfish, pelagic and non-pelagic rockfish, black rockfish and yelloweye rockfish.

A current challenge at this juncture is how to accommodate the prohibition on retaining yelloweye in Southeast from 2020 through 2024. Because it is closed to retention the port sampling data is not reflective of releases while remaining an accurate description of the harvest. Current modelling efforts revolve around developing a separate yelloweye curve that censors the missing data.

Process equations

The true harvest \(H_{ay}\) of rockfish for area \(a\) during year \(y\) is assumed to follow a temporal trend defined by a penalized spline:

\[\begin{equation} \textrm{log}(H_{ay})~\sim~\textrm{Normal}(f(a,y), {\sigma_H}) \end{equation}\]

where \(f(a,y)\) in a p-spline basis with 7 components (knots) and a second degree penalty. The variance, \(\sigma_H\), was given a normal prior with a mean and standard deviation of 0.25 and 1, respectively.

Charter and private harvest \(H_{ayu}\) (where u = 1 for charter anglers and u = 2 for private anglers) is a fraction of total annual harvest in each area:

\[\begin{equation} H_{ay1}~=~H_{ay}P_{(user)ay1}\\H_{ay2}~=~H_{ay}(1-P_{(user)ay1}) \end{equation}\]

where \(P_{(user)ay1}\) is the fraction of the annual harvest in each area taken by charter anglers. \(P_{(user)ay1}\) was modeled hierarchically across years as:

\[\begin{equation} P_{(user)ay1}~\sim~\textrm{beta}(\lambda1_a, \lambda2_a) \end{equation}\]

with non-informative priors on both parameters.

Annual black rockfish harvest \(H_{(black)ayu}\) for each area and user group is:

\[\begin{equation} H_{(black)ayu}~=~H_{ayu}P_{(pelagic)ayu}P_{(black|pelagic)ayu} \end{equation}\]

where \(P_{(pelagic)ayu}\) is the fraction of the annual harvest for each area and user group that was pelagic rockfish and \(P_{(black|pelagic)ayu}\) is the fraction of the annual harvest of pelagic rockfish for each area and user group that was black rockfish. Annual yelloweye rockfish harvest \(H_{(yelloweye)ayu}\) for each area and user group:

\[\begin{equation} H_{(yelloweye)ayu}~=~H_{ayu}(1-P_{(pelagic)ayu})P_{(yelloweye|non-pelagic)ayu} \end{equation}\]

where \(P_{(yellow|non-pelagic)ayu}\) is the fraction of the annual harvest of non-pelagic rockfish for each area and user group that was yelloweye rockfish. The composition parameters (\(P_{(pelagic)ayu}\), \(P_{(black|pelagic)ayu}\), \(P_{(yelloweye|non-pelagic)ayu}\)) were modeled using a logistic curve that would allow hindcasting without extrapolating beyond the limit of observed values such that:

\[\begin{equation} \textrm{logit}(P_{(comp)ayu})~=~\beta1_{(comp)ayu} + \frac{\beta2_{(comp)ayu}}{(1 + exp(\beta3_{(comp)ayu}*(y - \beta4_{(comp)ayu})))} + \beta5_{(comp)ayu}*I(u=private)+re_{(comp)ayu} \end{equation}\]

where the \(\beta\) parameters define the intercept, scaling factor, slope, inflection point and private angler effect, respectively, \(y\) is the year index, \(I(u=private)\) is an index variable which is 1 when the user groups is private and 0 otherwise and \(re_{(comp)ayu}\) is a random effect with a non-informative prior.

The true number of released rockfish \(R_{ayu}\) were based on the proportion of the total catch harvested by area, year, user group and species grouping (pelagic or yelloweye), \(pH_{(comp)ayu}\). Thus, converting \(H_{(comp)ayu}\) to total catches by user group, \(C_{(comp)ayu}\), with \(pH_{(comp)ayu}\) results in estimates of total releases such that

\[\begin{equation} R_{(comp)ayu}~=~ C_{(comp)ayu} - H_{(comp)ayu} ~=~ \frac{H_{(comp)ayu}}{pH_{(comp)ayu}} - H_{(comp)ayu} \end{equation}\]

with total releases calculated from the sum of the user group releases. The proportion of total rockfish harvested by user group, \(pH_{ayu}\), was assumed to be the mean of \(pH_{(pelagic)ayu}\), \(pH_{(yelloweye)ayu}\) and \(pH_{(nonpel-nonYE)ayu}\) weighted by the relative harvest \(H_{(comp)ayu}\) such that

\[\begin{equation} R_{ayu}~=~ \frac{\sum ({H_{(comp)ayu} * pH_{(comp)ayu})}}{\sum {H_{(comp)ayu}}} \end{equation}\]

The proportion harvest parameters \(pH_{(comp)ayu}\) were modeled using a logistic curve that would allow hindcasting based on trends in the data without extrapolating beyond the range of observed values such that

\[\begin{equation} \textrm{logit}(pH_{ayuc})~=~\beta1_{(pH)ayuc} + \frac{\beta2_{(pH)ayuc}}{(1 + exp(\beta3_{(pH)ayuc}*(y - \beta4_{(pH)ayuc}))) + \beta34_{(pH)ayuc}} \end{equation}\]

A random effect term allowed estimation during the historical period when data is available, but the curve defined by the above equation determined release estimates between 1977 and 1990.

Observation equations

SWHS estimates of annual rockfish harvest \(\widehat{SWHS}_H{ay}\) were assumed to index true harvest:

\[\begin{equation} \widehat{SWHS}_H{ay}~\sim~\textrm{LogNormal}\left(\textrm{log}(H_{ay}b_{ay}), \sigma_{SWHSHay}^2\right) \end{equation}\]

where bias in the SWHS harvest estimates \(b_H{ay}\) is modeled hierarchically across years as:

\[\begin{equation} b_H{ay}~\sim~\textrm{Normal}(\mu_H{(b)a}, \sigma_H{(b)a}) \end{equation}\]

with non-informative priors on both parameters.

SWHS estimates of guided angler harvest \(\widehat{SWHS}_H{ay1}\) are related to total harvest by:

\[\begin{equation} \widehat{SWHS}_H{ay1}~\sim~\textrm{LogNormal}\left(\textrm{log}(H_{ay1}b_{ay}), \sigma_{SWHS_{ay1}}^2\right) \end{equation}\]

Reported guide logbook harvest \(\widehat{LB}_H{ay}\) is related to true harvest as:

\[\begin{equation} \widehat{LB}_H{ay}~\sim~\textrm{Poisson}(H_{ay1})\\ \widehat{LB}_H{(pelagic)ay}~\sim~\textrm{Poisson}(H_{ay1}P_{(pelagic)ay1})\\ \widehat{LB}_H{(yelloweye)ay}~\sim~\textrm{Poisson}(H_{(yelloweye)ay1})\\ \end{equation}\]

SWHS estimates of annual rockfish releases \(\widehat{SWHS}_R{ay}\) were assumed to index true releases in a similar fashion and thus modeled similarly. Because logbook release data is more questionable and demonstrates greater disagreement with SWHS estimates (Figure 1), several approaches have been explored. In the first approach model \(LB_{fit}\) treats the release data as a true census and the releases are related to true releases just as harvests were modelled such that:

\[\begin{equation} \widehat{LB}_R{ay}~\sim~\textrm{Poisson}(R_{ay1})\\ \widehat{LB}_R{(pelagic)ay}~\sim~\textrm{Poisson}(R_{ay1}P_{(pelagic)ay1})\\ \widehat{LB}_R{(yelloweye)ay}~\sim~\textrm{Poisson}(R_{(yelloweye)ay1})\\ \end{equation}\]

In the second approach we consider the logbook release data to be a minimal estimate of the true releases. Thus model \(LB_{cens}\) censors the release data (censored data is entered as NA) and treats the reported releases as a minimal number such that

\[\begin{equation} \text{censored} \widehat{LB}_R{ay}~\sim~\textrm{LogNormal}\left(\log(R_{ay}), 1\right)\text{T}\left(\widehat{LB}_R{ay}, \infty\right)\\ \text{censored} \widehat{LB}_R{(pelagic)ay}~\sim~\textrm{LogNormal}\left(\log(R_{(pelagic)ay}), 1\right)\text{T}\left(\widehat{LB}_R{(pelagic)ay}, \infty\right)\\ \text{censored} \widehat{LB}_R{(ye)ay}~\sim~\textrm{LogNormal}\left(\log(R_{(ye)ay}), 1\right)\text{T}\left(\widehat{LB}_R{(ye)ay}, \infty\right)\\ \end{equation}\]

Model \(LB_{hyb}\) is a hybrid approach that treats the yelloweye releases as a reliable census of yelloweye releases (given the emphasis and ease of recording these fish) but censors the pelagic and total rockfish release estimates such that

\[\begin{equation} \text{censored} \widehat{LB}_R{ay}~\sim~\textrm{LogNormal}\left(\log(R_{ay}), 1\right)\text{T}\left(\widehat{LB}_R{ay}, \infty\right) \\ \text{censored} \widehat{LB}_R{(pelagic)ay}~\sim~\textrm{LogNormal}\left(\log(R_{(pelagic)ay}), 1\right)\text{T}\left(\widehat{LB}_R{(pelagic)ay}, \infty\right) \\ \widehat{LB}_R{(yelloweye)ay}~\sim~\textrm{Poisson}(R_{(yelloweye)ay1})\\ \end{equation}\]

SWHS estimates of guided angler release \(\widehat{SWHS}_R{ay1}\) is modeled the same as harvests.

SWHS release bias was modeled differently in the \(LB_{fit}\), \(LB_{cens}\), and \(LB_{hyb}\) models. Because the \(LB_{fit}\) model assumes that logbook release data is true and the poison likelihoods assume a much smaller variance than the large variances associated with the SWHS release estimates, SWHS release estimates \(b_R{ay}\) were modelled independently of the harvest bias \(b_H{ay}\) such that

\[\begin{equation} b_R{ay}~\sim~\textrm{Normal}(\mu_R{(b)a}, \sigma_R{(b)a}) \end{equation}\]

where bias in the SWHS release estimates \(b_R{ay}\) is modeled hierarchically across years as:

\[\begin{equation} b_R{ay}~\sim~\textrm{Normal}(\mu_R{(b)a}, \sigma_R{(b)a}) \end{equation}\]

with non-informative priors on both parameters.

The \(LB_{cens}\) model treats the logbook release data as lower bound on the release estimates and thus the likelihood linking true releases to the SWHS release estimates is dominant. During model development it was apparent that estimating bias in the SWHS data was more difficult and a different structure was employed that assumed bias in SWHS release data followed a similar pattern to that of the harvests but is offset by some area specific amount. In these models \(b_R{ay}\) differed from \(b_H{ay}\) by offset \(Rboff_{a}\) such that

\[\begin{equation} b_R{ay}~=~b_H{ay} + Rboff_{ay} \end{equation}\]

where

\[\begin{equation} Rboff_{a}~\sim~\textrm{Normal}(\mu_{(bR)r}, \sigma_{(bR)r}) \end{equation}\]

such that \(Rboff_{a}\) was modeled hierarchically across region r.

The number of pelagic rockfish sampled in harvest sampling programs \(x_{(pelagic)ayu}\) follow a binomial distribution:

\[\begin{equation} x_{(pelagic)ayu}~\sim~\textrm{Binomial}(P_{(pelagic)ayu}, N_{ayu}) \end{equation}\]

where \(N_{ayu}\) is the total number of rockfish sampled in area \(a\) during year \(y\) form user group \(u\). The number of black rockfish sampled in harvest sampling programs and the number of yellow rockfish sampled modeled analogously with an appropriately substituted \(N\).

Unresolved issues and outstanding questions:

Models detailed in this markdown represent the next step in the modelling process whereby the pH parameters are separated out by species. This approach separates the compositional data that is germaine to the harvests from the release estimates and releases are now based on pH. Additionally, this approach allows the pH parameters to differ between pelagic and yelloweye which is appropriate given regulatory changes as well as fisherman and industry behaviour and is born out in the results. The approach results in great uncertainty around unguided release estimates, but that uncertainty is appropriate given the data. These models handle the yelloweye closures in southeast much more appropriately given that the compositional data is no longer directly applied to the release estimates. These versions of the model are in development and it is unclear whether the \(LB_{cens}\) model would work, but it appears applicable to the \(LB_{fit}\) and \(LB_{hyb}\) approaches.

Other issues include:

  1. Complete convergence has not been achieved and the logistic curve parameters for p_pelagic and p_yellow remain the last sticking point. I think that p_pelagic will resolve with longer chains.
  2. Estimate precision: These models are producing more precise harvest estimates that in Adam’s original model. I am not sure why at this juncture. sigma_H on the spline was switched from a fixed value to a prior centered around that fixed value, but the model estimates are in the same range as the fixed value. Would the number of knots in the spline explain this? 7 knots was settled on during early model fitting when it clearly performed better than fewer or more knots.
  3. Prior choices in general need to be vetted. The priors on the logistic curves are fairly informed in an effort to achieve the desired shapes for hindcasting. Ideally, sensitivity testing would occur but the model is very slow to converge. The beta parameters on the logistic curves have required a lot of work on the priors to reach convergence.
  4. Random effects on pH: These are currently used in the model but because pH isn’t linked to data as the p_comp data is I am not sure what to make of them or if they are appropriate.
  5. Model comparisons: I need to write code for comparing models side by side as well as quantifying the differences between these methods and the Howard methods.

Results

**Figure X.**- Rhat values and proportion of parameters that converged (Rhat < 1.1.)

Figure X.- Rhat values and proportion of parameters that converged (Rhat < 1.1.)

Estimate comparison

Since previous estimates of rockfish harvest have been produced these first 3 graphs will be used to show how the modeled estimates compare to the estimates produced earlier. For total rockfish the estimates are in general agreement although differences are noted. These estimates should be more reliable because they include both SWHS and guide logbook data, handle variance more appropriately, use hierarchical distributions when data is missing, directly consider observation error and are produced using reproducible research.

**Figure 2.**- Total rockfish harvests 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 2.- Total rockfish harvests 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.



**Figure 3.**- Total rockfish releases 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 3.- Total rockfish releases 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.


Notes from Adam: When looking at only black rockfish the most significant differences are for the Prince William Sound Inside area. I did not spend a great deal of time tracking this down although it looks like the previous version used bad values for \(P_{(black)ayu}\) for at least unguided anglers. For the moment I would ignore the results for BSIA and SOKO2SAP. I think it is possible to give approximate values for these areas but it will require a little more coding which I have yet to do.

**Figure 4.**- Black rockfish harvests 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 4.- Black rockfish harvests 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.


And black rockfish releases…

**Figure 5.**- Black rockfish releases 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 5.- Black rockfish releases 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.



**Figure 6.**- Yellow rockfish harvests 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 6.- Yellow rockfish harvests 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.



**Figure 7.**- Yellow rockfish releases 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 7.- Yellow rockfish releases 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Model fit

Logbook residuals

**Figure 8.**- Residuals from logbook harvests

Figure 8.- Residuals from logbook harvests


SWHS residuals

**Figure 9.**- Residuals from SWHS harvests.

Figure 9.- Residuals from SWHS harvests.



**Figure 10.**- Residual of SWHS releases

Figure 10.- Residual of SWHS releases

Parameter estimates

P(Charter)

These histograms show the posterior distribution of the mean percent of rockfish harvested by the charter fleet.

**Figure 11.**- Mean percent of harvest by charter anglers.

Figure 11.- Mean percent of harvest by charter anglers.


When considered annually we see the percent of rockfish harvested by the charter fleet follows our data fairly well although we just do not have much information about this ratio. Prior to 2011 the percent charter is confounded with SWHS bias and should be mostly discounted.

**Figure 12.**- Annual estimates of the percent of harvest by charter anglers for 16 commerical fishing manamgent areas, 1996-2023.

Figure 12.- Annual estimates of the percent of harvest by charter anglers for 16 commerical fishing manamgent areas, 1996-2023.

P(Harvest)

These plots show the fitted logistic line to the proportion of caught rockfish that are harvested. These estimates are used for hindcasting catch estimates based on the harvest data in early years when catch estimates are unavailable.

**Figure 13.**- Annual proportion of all rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of all rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.


**Figure 13.**- Annual proportion of pelagic rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of pelagic rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.


**Figure 13.**- Annual proportion of yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.


**Figure 13.**- Annual proportion of non-pelagic, non-yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of non-pelagic, non-yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

SWHS bias

Figure 14 shows the mean estimate for SWHS bias. Cook Inlet, North Gulf Coast and North Southeast Inside all look pretty good while most other areas have substantial bias. Prince William Sound Inside has the largest bias.

**Figure 14.**- Mean SWHS bias for harvests and catches. Note that a bias < 1 indicates that the SWHS *underestimates* the true value and bias > 1 indicates the survey *overestimates* the true value.

Figure 14.- Mean SWHS bias for harvests and catches. Note that a bias < 1 indicates that the SWHS underestimates the true value and bias > 1 indicates the survey overestimates the true value.


Our estimates of SWHS bias track observations fairly well when he have guided harvest estimates. There are some disturbing trends/patterns seen in the earlier time periods. Often the patterns represent periods where SWHS estimates and guide logbook estimates do not follow the recent relationship. I’m not sure what drives the trends but it seems plausible to me that long-term changes in the ratio of charter and private anglers may be a factor. If Charter/Private ratio information is available in the historical creel data it my be helpful here (particularly for North Southeast Inside and South Southeast outside).

**Figure 15.**- Annual estimates of SWHS bias in harvests and releases for 16 commerical fishing manamgent areas, 1996-2023. Note that a bias < 1 indicates that the SWHS *underestimates* the true value and bias > 1 indicates the survey *overestimates* the true value.

Figure 15.- Annual estimates of SWHS bias in harvests and releases for 16 commerical fishing manamgent areas, 1996-2023. Note that a bias < 1 indicates that the SWHS underestimates the true value and bias > 1 indicates the survey overestimates the true value.

P(pelagic)

We model the percentage of pelagic rockfish in the harvest because we have the information for charter anglers (via logbooks) starting in 1998. Other than looking at the model estimates you can use Figure 8 to compare the two data streams for pelagic rockfish harvest. In general they are in agreement with major exceptions in Price William Sound inside, Prince William Sound outside (early in the time series) and South Southeast inside.

**Figure 16.**- Annual estimates of the percent of the sport harvest that was pelagic rockfish for 16 commerical fishing manamgent areas, 1996-2023.

Figure 16.- Annual estimates of the percent of the sport harvest that was pelagic rockfish for 16 commerical fishing manamgent areas, 1996-2023.

P(black|pelagic)

Note that in Southeast Alaska we only have composition data starting in 2006. Tania dug up old SE data, but it did not provide any useful data for species apportionment.

**Figure 17.**- Annual estimates of the percent of the sport harvest of pelagic rockfish that were black rockfish for 16 commerical fishing manamgent areas, 1996-2023.

Figure 17.- Annual estimates of the percent of the sport harvest of pelagic rockfish that were black rockfish for 16 commerical fishing manamgent areas, 1996-2023.

P(yelloweye|non-pelagic)

**Figure 18.**- Annual estimates of the percent of the sport harvest of non-pelagic rockfish that were yelloweye rockfish for 16 commerical fishing manamgent areas, 1996-2023.

Figure 18.- Annual estimates of the percent of the sport harvest of non-pelagic rockfish that were yelloweye rockfish for 16 commerical fishing manamgent areas, 1996-2023.



Summary of unconverged parameters:

Table 1. Summary of unconverged parameters including the number (n) and the average Rhat from the unconverged parameters.
parameter n badRhat_avg
beta3_yellow 1 82.953328
sd_comp 1 3.911792
beta1_pH 3 3.508365
beta2_yellow 4 2.545069
beta3_pH 6 2.387490
beta1_yellow 3 2.161980
beta4_yellow 1 2.124066
beta2_pH 5 1.731435
tau_beta0_pH 4 1.678775
beta0_pelagic 4 1.544596
beta3_pelagic 2 1.462183
parameter n badRhat_avg
beta0_pH 12 1.449195
beta2_pelagic 2 1.432517
beta1_pelagic 4 1.390422
beta4_pelagic 1 1.285203
tau_beta0_yellow 2 1.255500
beta0_yellow 6 1.220557
tau_beta0_pelagic 2 1.207798
mu_beta0_pH 1 1.183426
beta_H 1 1.135480
mu_beta0_pelagic 1 1.123436
Table 2. Summary of unconverged parameters by area
afognak BSAI CI CSEO EWYKT NG NSEI NSEO PWSI PWSO SOKO2SAP SSEI SSEO WKMA
beta_H 1 0 0 0 0 0 0 0 0 0 0 0 0 0
beta0_pelagic 0 0 0 1 0 1 0 1 0 0 0 0 0 1
beta0_pH 0 1 0 1 0 0 1 1 0 0 1 1 1 1
beta0_yellow 0 0 0 1 1 1 0 1 0 0 1 0 0 1
beta1_pelagic 0 0 0 1 0 1 0 1 0 0 0 0 0 1
beta1_pH 0 0 0 1 0 0 0 0 0 0 0 1 0 0
beta1_yellow 0 0 0 1 0 1 0 0 0 0 0 0 0 1
beta2_pelagic 0 0 0 1 0 0 1 0 0 0 0 0 0 0
beta2_pH 0 0 0 1 0 0 0 0 0 1 1 0 0 1
beta2_yellow 0 0 0 1 1 0 0 0 0 1 0 0 0 1
beta3_pelagic 0 0 0 1 0 0 0 0 0 0 0 0 0 1
beta3_pH 0 0 0 1 0 0 0 0 1 0 0 1 1 1
beta3_yellow 0 0 0 1 0 0 0 0 0 0 0 0 0 0
beta4_pelagic 0 0 0 1 0 0 0 0 0 0 0 0 0 0
beta4_yellow 0 0 0 1 0 0 0 0 0 0 0 0 0 0
mu_beta0_pelagic 0 0 0 0 0 1 0 0 0 0 0 0 0 0
mu_beta0_pH 0 0 0 0 0 1 0 0 0 0 0 0 0 0
sd_comp 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tau_beta0_pelagic 0 0 1 0 0 1 0 0 0 0 0 0 0 0
tau_beta0_pH 0 0 1 0 0 1 0 0 1 0 0 0 0 0
tau_beta0_yellow 0 0 1 0 0 0 0 0 1 0 0 0 0 0

Parameter estimates:

Summary Table of Parameter Estimates
Parameter mean sd Lower_CI Median Upper_CI
mu_bc_H[1] -0.125 0.077 -0.264 -0.129 0.038
mu_bc_H[2] -0.104 0.042 -0.178 -0.107 -0.012
mu_bc_H[3] -0.448 0.070 -0.581 -0.447 -0.305
mu_bc_H[4] -0.977 0.191 -1.368 -0.972 -0.609
mu_bc_H[5] 0.723 0.756 -0.270 0.586 2.541
mu_bc_H[6] -2.201 0.325 -2.835 -2.200 -1.553
mu_bc_H[7] -0.477 0.110 -0.706 -0.473 -0.273
mu_bc_H[8] 0.282 0.375 -0.355 0.240 1.134
mu_bc_H[9] -0.303 0.135 -0.559 -0.303 -0.035
mu_bc_H[10] -0.112 0.070 -0.245 -0.114 0.027
mu_bc_H[11] -0.113 0.041 -0.192 -0.115 -0.030
mu_bc_H[12] -0.265 0.105 -0.485 -0.261 -0.060
mu_bc_H[13] -0.118 0.078 -0.266 -0.120 0.039
mu_bc_H[14] -0.282 0.094 -0.464 -0.282 -0.105
mu_bc_H[15] -0.354 0.054 -0.453 -0.356 -0.246
mu_bc_H[16] -0.139 0.384 -0.783 -0.169 0.724
mu_bc_R[1] 1.471 0.178 1.112 1.474 1.810
mu_bc_R[2] 1.495 0.075 1.338 1.497 1.633
mu_bc_R[3] 1.415 0.144 1.127 1.422 1.691
mu_bc_R[4] 0.974 0.225 0.490 0.988 1.365
mu_bc_R[5] 1.192 0.434 0.332 1.196 2.043
mu_bc_R[6] -1.562 0.442 -2.428 -1.556 -0.695
mu_bc_R[7] 0.508 0.241 0.036 0.514 0.959
mu_bc_R[8] 0.542 0.197 0.162 0.541 0.930
mu_bc_R[9] 0.466 0.194 0.044 0.484 0.796
mu_bc_R[10] 1.377 0.174 1.043 1.372 1.728
mu_bc_R[11] 1.188 0.078 1.026 1.189 1.337
mu_bc_R[12] 0.991 0.201 0.547 1.007 1.345
mu_bc_R[13] 1.024 0.095 0.834 1.023 1.213
mu_bc_R[14] 1.060 0.155 0.728 1.065 1.340
mu_bc_R[15] 0.836 0.100 0.642 0.834 1.033
mu_bc_R[16] 1.134 0.121 0.909 1.131 1.380
tau_pH[1] 3.730 0.339 3.101 3.724 4.430
tau_pH[2] 0.957 0.345 0.604 0.776 1.667
tau_pH[3] 3.466 0.433 2.654 3.454 4.374
beta0_pH[1,1] 0.607 0.191 0.217 0.614 0.965
beta0_pH[2,1] 1.197 0.209 0.786 1.203 1.602
beta0_pH[3,1] 1.156 0.288 0.532 1.181 1.615
beta0_pH[4,1] 1.369 0.371 0.546 1.405 1.995
beta0_pH[5,1] -1.307 0.356 -2.038 -1.300 -0.658
beta0_pH[6,1] -0.868 0.690 -2.401 -0.737 0.195
beta0_pH[7,1] 0.183 0.893 -2.045 0.659 0.932
beta0_pH[8,1] -0.975 0.447 -2.213 -0.895 -0.362
beta0_pH[9,1] -1.290 0.510 -2.392 -1.257 -0.322
beta0_pH[10,1] 0.477 0.220 0.008 0.487 0.875
beta0_pH[11,1] 0.108 0.907 -1.531 0.418 1.388
beta0_pH[12,1] 0.620 0.196 0.208 0.628 0.990
beta0_pH[13,1] -0.658 0.228 -1.121 -0.646 -0.233
beta0_pH[14,1] -1.143 0.262 -1.668 -1.132 -0.635
beta0_pH[15,1] -1.419 0.343 -2.089 -1.414 -0.764
beta0_pH[16,1] -2.079 0.436 -2.716 -2.123 -1.161
beta0_pH[1,2] 2.615 0.387 1.833 2.650 3.245
beta0_pH[2,2] 2.779 0.330 2.019 2.809 3.354
beta0_pH[3,2] 2.627 0.448 1.770 2.625 3.450
beta0_pH[4,2] 2.682 0.278 2.074 2.702 3.181
beta0_pH[5,2] 4.022 2.171 1.707 3.570 8.838
beta0_pH[6,2] 2.096 1.417 -1.656 2.663 3.534
beta0_pH[7,2] 1.690 0.805 -1.047 1.895 2.472
beta0_pH[8,2] 2.522 0.780 0.043 2.726 3.286
beta0_pH[9,2] 2.843 0.869 0.769 2.998 4.016
beta0_pH[10,2] 3.305 0.788 1.382 3.518 4.283
beta0_pH[11,2] -2.698 0.098 -2.749 -2.730 -2.458
beta0_pH[12,2] -2.708 0.071 -2.750 -2.731 -2.531
beta0_pH[13,2] -2.706 0.068 -2.749 -2.730 -2.511
beta0_pH[14,2] -2.716 0.046 -2.750 -2.733 -2.586
beta0_pH[15,2] -2.708 0.061 -2.749 -2.730 -2.531
beta0_pH[16,2] -2.712 0.055 -2.749 -2.731 -2.548
beta0_pH[1,3] 1.116 0.369 0.178 1.177 1.603
beta0_pH[2,3] 1.424 0.789 -0.691 1.570 2.420
beta0_pH[3,3] 2.059 0.282 1.523 2.054 2.645
beta0_pH[4,3] 2.038 0.790 0.142 2.135 3.050
beta0_pH[5,3] 0.317 1.540 -1.164 0.008 4.401
beta0_pH[6,3] -0.414 0.928 -2.457 -0.255 0.791
beta0_pH[7,3] -0.247 0.619 -2.187 -0.082 0.471
beta0_pH[8,3] -0.215 0.234 -0.692 -0.206 0.204
beta0_pH[9,3] -0.039 0.459 -1.027 -0.023 0.830
beta0_pH[10,3] -0.040 0.495 -1.239 0.003 0.795
beta0_pH[11,3] 0.086 0.356 -0.697 0.100 0.716
beta0_pH[12,3] -2.553 0.173 -2.744 -2.600 -2.100
beta0_pH[13,3] 0.382 0.334 -0.309 0.405 0.986
beta0_pH[14,3] 0.224 0.239 -0.248 0.234 0.666
beta0_pH[15,3] 0.030 0.320 -0.738 0.053 0.599
beta0_pH[16,3] 0.279 0.327 -0.327 0.266 1.088
beta1_pH[1,1] 2.996 0.390 2.301 2.964 3.831
beta1_pH[2,1] 2.379 0.388 1.769 2.325 3.355
beta1_pH[3,1] 2.537 0.594 1.677 2.435 3.961
beta1_pH[4,1] 3.249 0.831 2.027 3.090 5.356
beta1_pH[5,1] 2.582 0.398 1.851 2.576 3.396
beta1_pH[6,1] 4.142 1.231 2.119 4.035 6.743
beta1_pH[7,1] 3.098 1.691 0.723 2.635 6.961
beta1_pH[8,1] 4.380 1.152 2.783 4.180 7.561
beta1_pH[9,1] 2.842 0.545 1.794 2.813 3.993
beta1_pH[10,1] 1.984 0.305 1.445 1.959 2.646
beta1_pH[11,1] 3.121 0.922 1.789 2.833 4.809
beta1_pH[12,1] 2.373 0.239 1.914 2.371 2.859
beta1_pH[13,1] 3.689 0.324 3.122 3.671 4.408
beta1_pH[14,1] 4.164 0.314 3.575 4.157 4.799
beta1_pH[15,1] 4.087 0.393 3.365 4.079 4.869
beta1_pH[16,1] 5.652 0.556 4.515 5.689 6.622
beta1_pH[1,2] 1.669 1.505 0.081 1.249 5.883
beta1_pH[2,2] 1.750 1.733 0.061 1.112 6.502
beta1_pH[3,2] 1.425 1.108 0.138 1.235 4.849
beta1_pH[4,2] 2.453 1.912 0.099 2.065 7.065
beta1_pH[5,2] 3.093 1.954 0.224 2.815 7.629
beta1_pH[6,2] 2.167 1.403 0.198 1.874 5.508
beta1_pH[7,2] 1.899 1.719 0.049 1.389 6.079
beta1_pH[8,2] 1.655 1.654 0.043 1.098 6.169
beta1_pH[9,2] 1.740 1.335 0.067 1.497 5.407
beta1_pH[10,2] 1.981 1.717 0.087 1.468 6.456
beta1_pH[11,2] 3.582 0.803 2.541 3.224 5.000
beta1_pH[12,2] 4.803 0.402 4.077 4.780 5.656
beta1_pH[13,2] 5.243 0.306 4.630 5.242 5.828
beta1_pH[14,2] 4.637 0.323 4.005 4.638 5.266
beta1_pH[15,2] 5.258 0.302 4.658 5.269 5.828
beta1_pH[16,2] 5.432 0.301 4.827 5.429 6.032
beta1_pH[1,3] 2.093 0.605 1.312 1.991 3.904
beta1_pH[2,3] 1.557 1.233 0.199 1.161 5.087
beta1_pH[3,3] 0.914 0.457 0.218 0.891 1.634
beta1_pH[4,3] 1.532 1.247 0.095 1.243 4.914
beta1_pH[5,3] 4.578 1.773 1.236 4.431 8.461
beta1_pH[6,3] 3.226 1.932 0.268 2.999 7.570
beta1_pH[7,3] 1.447 1.510 0.068 0.884 5.883
beta1_pH[8,3] 3.114 0.390 2.370 3.115 3.885
beta1_pH[9,3] 1.843 0.814 0.395 1.799 3.595
beta1_pH[10,3] 3.341 0.611 2.280 3.296 4.686
beta1_pH[11,3] 2.676 0.411 1.910 2.658 3.552
beta1_pH[12,3] 5.888 0.263 5.312 5.905 6.362
beta1_pH[13,3] 1.728 0.364 1.058 1.706 2.475
beta1_pH[14,3] 2.277 0.291 1.714 2.273 2.845
beta1_pH[15,3] 1.894 0.354 1.226 1.882 2.683
beta1_pH[16,3] 1.584 0.403 0.882 1.572 2.262
beta2_pH[1,1] 0.515 0.316 0.252 0.460 1.039
beta2_pH[2,1] 0.928 0.971 0.201 0.637 4.024
beta2_pH[3,1] 0.834 1.022 0.156 0.473 4.008
beta2_pH[4,1] 0.391 0.423 0.126 0.303 1.217
beta2_pH[5,1] 3.441 1.664 1.122 3.079 7.494
beta2_pH[6,1] 0.218 0.234 0.085 0.175 0.547
beta2_pH[7,1] -1.601 3.725 -7.415 -2.664 6.487
beta2_pH[8,1] 0.234 0.094 0.094 0.220 0.470
beta2_pH[9,1] 0.595 0.536 0.221 0.475 1.785
beta2_pH[10,1] 1.085 1.059 0.300 0.758 4.116
beta2_pH[11,1] 1.275 1.090 0.462 0.889 4.617
beta2_pH[12,1] 3.088 1.530 1.083 2.734 6.949
beta2_pH[13,1] 0.719 0.284 0.355 0.657 1.419
beta2_pH[14,1] 1.042 0.448 0.584 0.941 2.136
beta2_pH[15,1] 0.735 0.292 0.404 0.674 1.413
beta2_pH[16,1] 0.388 0.115 0.232 0.365 0.681
beta2_pH[1,2] -0.312 3.222 -6.884 0.107 5.818
beta2_pH[2,2] -2.001 2.708 -7.379 -1.883 3.882
beta2_pH[3,2] -2.501 2.484 -7.745 -2.213 2.504
beta2_pH[4,2] -2.841 2.351 -7.722 -2.566 1.607
beta2_pH[5,2] 0.083 3.162 -6.644 0.436 5.959
beta2_pH[6,2] -0.952 3.237 -7.000 -1.065 5.607
beta2_pH[7,2] -1.935 2.900 -7.634 -1.929 4.357
beta2_pH[8,2] -1.425 3.039 -7.200 -1.419 4.615
beta2_pH[9,2] -1.876 2.940 -7.477 -1.942 4.398
beta2_pH[10,2] -1.381 3.114 -7.470 -1.340 4.920
beta2_pH[11,2] 0.137 3.972 -7.817 1.410 6.292
beta2_pH[12,2] -2.194 1.563 -6.424 -1.682 -0.555
beta2_pH[13,2] -3.711 1.792 -8.079 -3.361 -1.243
beta2_pH[14,2] -3.362 1.706 -7.577 -2.972 -1.101
beta2_pH[15,2] -4.758 1.952 -9.156 -4.593 -1.691
beta2_pH[16,2] -5.133 1.904 -9.349 -4.947 -2.046
beta2_pH[1,3] 2.509 1.825 0.230 2.127 6.882
beta2_pH[2,3] 1.216 2.565 -5.162 1.010 6.338
beta2_pH[3,3] -2.572 2.590 -7.495 -2.600 3.859
beta2_pH[4,3] 0.979 2.861 -5.346 1.069 6.594
beta2_pH[5,3] 2.407 2.045 -0.190 2.102 7.021
beta2_pH[6,3] 2.195 2.450 -3.641 2.046 7.110
beta2_pH[7,3] 0.360 3.110 -6.245 0.534 6.190
beta2_pH[8,3] 3.917 2.045 0.873 3.687 8.388
beta2_pH[9,3] 2.064 2.165 -2.775 1.805 6.601
beta2_pH[10,3] 1.020 1.038 0.295 0.647 4.341
beta2_pH[11,3] -1.550 1.074 -4.615 -1.209 -0.488
beta2_pH[12,3] -1.332 0.325 -2.065 -1.280 -0.860
beta2_pH[13,3] -2.435 1.557 -6.385 -2.034 -0.591
beta2_pH[14,3] -2.801 1.477 -6.476 -2.455 -0.885
beta2_pH[15,3] -2.275 1.495 -6.217 -1.875 -0.594
beta2_pH[16,3] -2.586 1.648 -6.713 -2.167 -0.631
beta3_pH[1,1] 36.059 1.001 34.206 36.003 38.159
beta3_pH[2,1] 33.086 1.267 31.048 32.941 36.190
beta3_pH[3,1] 34.149 1.415 31.821 33.993 37.491
beta3_pH[4,1] 35.207 1.993 31.998 34.909 40.030
beta3_pH[5,1] 27.217 0.415 26.467 27.169 28.018
beta3_pH[6,1] 38.722 3.332 31.730 38.969 44.408
beta3_pH[7,1] 21.353 2.256 19.145 20.295 25.087
beta3_pH[8,1] 39.591 2.042 35.297 39.625 43.632
beta3_pH[9,1] 29.084 1.480 26.549 28.959 32.243
beta3_pH[10,1] 33.257 1.215 30.988 33.197 35.785
beta3_pH[11,1] 30.880 1.006 29.072 30.877 32.850
beta3_pH[12,1] 30.456 0.453 29.601 30.450 31.262
beta3_pH[13,1] 32.725 0.565 31.668 32.715 33.861
beta3_pH[14,1] 31.566 0.432 30.774 31.545 32.436
beta3_pH[15,1] 30.717 0.549 29.652 30.702 31.844
beta3_pH[16,1] 30.428 0.667 29.220 30.406 31.809
beta3_pH[1,2] 32.229 7.993 19.631 30.679 43.824
beta3_pH[2,2] 28.833 6.425 19.569 28.038 42.375
beta3_pH[3,2] 36.338 7.473 20.223 40.203 44.347
beta3_pH[4,2] 27.473 6.468 19.578 25.517 42.672
beta3_pH[5,2] 30.616 6.715 19.805 29.724 43.467
beta3_pH[6,2] 31.481 5.335 20.872 32.366 41.255
beta3_pH[7,2] 27.690 5.824 19.473 26.858 42.210
beta3_pH[8,2] 28.210 5.993 19.545 27.184 41.882
beta3_pH[9,2] 33.977 8.634 19.998 32.454 44.822
beta3_pH[10,2] 29.762 6.787 19.733 28.594 43.306
beta3_pH[11,2] 31.684 8.404 20.252 27.829 43.519
beta3_pH[12,2] 42.108 0.757 40.374 42.163 43.287
beta3_pH[13,2] 43.386 0.327 42.698 43.388 43.967
beta3_pH[14,2] 42.740 0.484 41.627 42.810 43.507
beta3_pH[15,2] 43.321 0.248 42.824 43.320 43.786
beta3_pH[16,2] 43.345 0.220 42.892 43.348 43.754
beta3_pH[1,3] 39.913 1.188 36.789 40.015 41.895
beta3_pH[2,3] 31.880 4.405 20.942 32.477 40.050
beta3_pH[3,3] 39.541 5.176 22.800 41.355 43.484
beta3_pH[4,3] 28.550 4.763 19.814 29.145 38.379
beta3_pH[5,3] 29.953 5.510 19.853 30.703 38.578
beta3_pH[6,3] 30.786 4.858 20.974 30.839 42.147
beta3_pH[7,3] 29.808 6.895 19.502 28.703 42.989
beta3_pH[8,3] 41.350 0.378 40.410 41.384 41.917
beta3_pH[9,3] 32.816 3.666 21.404 33.668 38.216
beta3_pH[10,3] 36.174 1.160 33.622 36.238 38.253
beta3_pH[11,3] 41.492 0.737 40.175 41.450 43.091
beta3_pH[12,3] 42.342 0.248 41.852 42.354 42.816
beta3_pH[13,3] 42.168 0.908 40.653 42.046 44.171
beta3_pH[14,3] 40.810 0.498 39.805 40.848 41.725
beta3_pH[15,3] 41.976 0.854 40.470 41.939 43.765
beta3_pH[16,3] 41.138 3.206 31.719 41.557 43.475
beta0_pelagic[1] 1.308 0.611 -0.091 1.351 2.280
beta0_pelagic[2] 1.186 0.447 -0.375 1.318 1.642
beta0_pelagic[3] 0.120 0.337 -0.761 0.186 0.592
beta0_pelagic[4] 0.171 0.360 -0.671 0.223 0.749
beta0_pelagic[5] 0.837 0.423 -0.071 0.881 1.506
beta0_pelagic[6] 1.272 0.406 0.334 1.363 1.806
beta0_pelagic[7] 0.517 1.638 -3.176 1.437 1.788
beta0_pelagic[8] 1.742 0.267 1.052 1.784 2.079
beta0_pelagic[9] 0.594 1.194 -2.235 1.000 2.080
beta0_pelagic[10] 2.155 0.507 0.899 2.295 2.805
beta0_pelagic[11] -0.102 0.626 -1.639 0.048 0.693
beta0_pelagic[12] 1.686 0.146 1.401 1.686 1.979
beta0_pelagic[13] 0.457 0.157 0.129 0.466 0.744
beta0_pelagic[14] 0.054 0.408 -1.424 0.145 0.515
beta0_pelagic[15] -0.375 0.138 -0.649 -0.379 -0.097
beta0_pelagic[16] 0.154 0.203 -0.283 0.167 0.522
beta1_pelagic[1] 1.041 0.625 0.053 0.984 2.471
beta1_pelagic[2] 0.402 0.432 0.015 0.258 1.839
beta1_pelagic[3] 0.994 0.488 0.390 0.872 2.306
beta1_pelagic[4] 1.051 0.374 0.433 0.996 1.911
beta1_pelagic[5] 0.814 0.534 0.046 0.760 2.012
beta1_pelagic[6] 0.727 0.819 0.029 0.518 3.065
beta1_pelagic[7] 1.592 1.713 0.024 0.659 5.170
beta1_pelagic[8] 0.921 0.754 0.071 0.743 3.006
beta1_pelagic[9] 2.519 1.201 0.993 2.155 5.288
beta1_pelagic[10] 0.798 0.847 0.024 0.507 3.162
beta1_pelagic[11] 3.039 1.018 1.624 2.851 5.449
beta1_pelagic[12] 2.777 0.327 2.152 2.771 3.445
beta1_pelagic[13] 1.772 0.360 1.182 1.732 2.614
beta1_pelagic[14] 2.917 0.755 1.844 2.783 5.145
beta1_pelagic[15] 2.123 0.246 1.635 2.122 2.619
beta1_pelagic[16] 3.122 0.494 2.314 3.068 4.183
beta2_pelagic[1] 2.190 2.104 -2.093 1.866 6.742
beta2_pelagic[2] 1.344 2.637 -4.581 1.238 6.760
beta2_pelagic[3] 1.471 1.674 0.079 0.711 5.863
beta2_pelagic[4] 2.150 1.753 0.264 1.610 6.610
beta2_pelagic[5] -2.084 2.451 -6.979 -1.930 3.630
beta2_pelagic[6] 1.368 2.777 -4.775 1.307 6.764
beta2_pelagic[7] 0.346 3.567 -6.254 0.019 6.992
beta2_pelagic[8] -2.014 2.070 -6.705 -1.574 0.561
beta2_pelagic[9] 1.180 1.523 0.126 0.490 5.773
beta2_pelagic[10] 1.329 2.411 -3.939 0.878 6.449
beta2_pelagic[11] 0.409 0.547 0.079 0.216 1.838
beta2_pelagic[12] 1.075 0.427 0.513 1.002 2.087
beta2_pelagic[13] 1.631 1.352 0.293 1.055 4.737
beta2_pelagic[14] 0.384 0.237 0.103 0.337 0.901
beta2_pelagic[15] 1.936 1.034 0.792 1.642 4.833
beta2_pelagic[16] 0.503 0.285 0.212 0.420 1.216
beta3_pelagic[1] 23.901 3.545 19.696 22.775 34.199
beta3_pelagic[2] 28.002 5.328 19.413 28.004 38.114
beta3_pelagic[3] 29.394 3.198 23.362 29.616 36.186
beta3_pelagic[4] 25.359 1.971 21.747 25.369 29.534
beta3_pelagic[5] 31.824 4.425 22.423 31.871 38.759
beta3_pelagic[6] 28.772 4.789 20.323 28.458 38.367
beta3_pelagic[7] 25.721 5.386 19.546 24.633 36.985
beta3_pelagic[8] 27.853 3.735 21.169 27.214 36.436
beta3_pelagic[9] 26.243 3.613 20.504 25.768 34.704
beta3_pelagic[10] 27.235 5.025 19.449 26.594 37.553
beta3_pelagic[11] 38.972 3.153 30.202 40.034 41.961
beta3_pelagic[12] 41.867 0.142 41.474 41.913 41.996
beta3_pelagic[13] 41.027 0.780 39.298 41.144 41.966
beta3_pelagic[14] 40.334 1.624 36.065 40.789 41.949
beta3_pelagic[15] 41.787 0.208 41.228 41.857 41.994
beta3_pelagic[16] 41.114 0.847 38.803 41.375 41.976
mu_beta0_pelagic[1] 0.638 0.725 -0.814 0.692 1.814
mu_beta0_pelagic[2] 1.091 0.888 -1.295 1.349 2.043
mu_beta0_pelagic[3] 0.299 0.463 -0.637 0.328 1.130
tau_beta0_pelagic[1] 3.348 5.159 0.077 1.588 21.221
tau_beta0_pelagic[2] 5.472 11.338 0.053 1.736 32.483
tau_beta0_pelagic[3] 1.731 1.269 0.232 1.420 4.970
beta0_yellow[1] -0.493 0.231 -0.961 -0.470 -0.151
beta0_yellow[2] 0.268 0.306 -0.617 0.337 0.649
beta0_yellow[3] -0.406 0.171 -0.752 -0.394 -0.098
beta0_yellow[4] 0.192 0.477 -0.812 0.241 0.956
beta0_yellow[5] -1.691 0.487 -2.621 -1.710 -0.657
beta0_yellow[6] -0.174 1.192 -3.329 0.271 1.124
beta0_yellow[7] -0.028 1.524 -3.122 0.094 1.978
beta0_yellow[8] 1.065 0.376 0.008 1.116 1.596
beta0_yellow[9] 0.019 0.545 -1.330 0.062 0.839
beta0_yellow[10] 0.621 0.184 0.257 0.619 0.992
beta0_yellow[11] -4.876 0.167 -4.998 -4.939 -4.367
beta0_yellow[12] -4.794 0.438 -4.998 -4.929 -3.585
beta0_yellow[13] -4.924 0.090 -4.999 -4.957 -4.675
beta0_yellow[14] -4.922 0.091 -4.999 -4.957 -4.676
beta0_yellow[15] -4.912 0.104 -4.999 -4.950 -4.611
beta0_yellow[16] -4.930 0.079 -4.999 -4.958 -4.705
beta1_yellow[1] 0.485 0.570 0.015 0.306 2.123
beta1_yellow[2] 1.500 0.672 0.807 1.279 3.433
beta1_yellow[3] 0.766 0.291 0.360 0.736 1.388
beta1_yellow[4] 2.775 1.083 1.072 2.675 5.004
beta1_yellow[5] 5.064 1.681 2.195 4.918 8.795
beta1_yellow[6] 2.843 1.513 0.506 2.647 6.564
beta1_yellow[7] 2.432 1.539 0.144 2.297 5.770
beta1_yellow[8] 2.102 1.352 0.425 1.732 5.497
beta1_yellow[9] 1.820 0.972 0.247 1.713 4.188
beta1_yellow[10] 2.437 0.542 1.483 2.394 3.593
beta1_yellow[11] 4.323 0.562 3.143 4.577 4.990
beta1_yellow[12] 7.317 1.143 4.046 7.508 8.828
beta1_yellow[13] 3.815 0.183 3.431 3.826 4.164
beta1_yellow[14] 4.723 0.187 4.338 4.731 5.078
beta1_yellow[15] 3.625 0.187 3.215 3.631 3.971
beta1_yellow[16] 4.576 0.184 4.198 4.584 4.916
beta2_yellow[1] -1.083 2.794 -6.539 -1.115 5.029
beta2_yellow[2] -1.652 1.581 -5.790 -1.176 -0.080
beta2_yellow[3] -2.223 1.689 -6.301 -1.859 -0.149
beta2_yellow[4] -0.261 0.539 -1.240 -0.134 -0.056
beta2_yellow[5] -3.480 1.826 -7.824 -3.171 -0.785
beta2_yellow[6] 2.125 2.793 -4.464 2.324 7.074
beta2_yellow[7] 1.071 2.241 -4.539 1.608 4.666
beta2_yellow[8] -2.136 2.046 -6.835 -1.632 -0.048
beta2_yellow[9] 2.047 2.661 -4.410 2.126 7.060
beta2_yellow[10] -3.303 2.026 -8.259 -2.855 -0.578
beta2_yellow[11] -2.073 5.508 -8.836 -4.720 7.865
beta2_yellow[12] -0.070 0.313 -0.134 -0.041 -0.010
beta2_yellow[13] -3.294 1.587 -7.953 -2.875 -1.413
beta2_yellow[14] -4.681 1.493 -8.282 -4.484 -2.373
beta2_yellow[15] -3.484 1.506 -7.202 -3.164 -1.430
beta2_yellow[16] -6.097 1.500 -9.269 -6.009 -3.411
beta3_yellow[1] 28.436 4.820 19.866 28.491 37.839
beta3_yellow[2] 29.300 2.003 24.357 29.180 33.373
beta3_yellow[3] 32.095 2.007 27.376 32.138 35.747
beta3_yellow[4] 30.003 3.601 22.711 30.080 36.705
beta3_yellow[5] 32.582 0.786 30.941 32.587 33.972
beta3_yellow[6] 34.078 5.757 24.748 37.388 41.233
beta3_yellow[7] 27.350 3.212 21.761 27.177 35.631
beta3_yellow[8] 31.678 3.095 24.232 32.828 37.013
beta3_yellow[9] 34.807 4.139 23.892 36.289 39.698
beta3_yellow[10] 29.193 0.731 27.392 29.296 30.236
beta3_yellow[11] 38.806 6.782 29.027 43.501 43.840
beta3_yellow[12] 34.376 3.573 29.269 33.827 43.035
beta3_yellow[13] 43.738 0.213 43.277 43.754 44.118
beta3_yellow[14] 43.547 0.183 43.222 43.547 43.893
beta3_yellow[15] 43.734 0.218 43.293 43.744 44.145
beta3_yellow[16] 43.601 0.145 43.320 43.603 43.868
mu_beta0_yellow[1] -0.108 0.398 -0.873 -0.110 0.690
mu_beta0_yellow[2] -0.048 0.684 -1.604 0.020 1.172
mu_beta0_yellow[3] -5.348 0.479 -6.391 -5.304 -4.624
tau_beta0_yellow[1] 8.018 17.093 0.250 3.685 45.976
tau_beta0_yellow[2] 0.854 0.910 0.068 0.599 3.005
tau_beta0_yellow[3] 38.472 33.039 1.129 30.973 128.002
beta0_black[1] -0.090 0.152 -0.389 -0.087 0.213
beta0_black[2] 1.679 0.331 0.698 1.747 2.063
beta0_black[3] 1.162 0.337 0.491 1.224 1.527
beta0_black[4] 1.831 0.377 0.769 1.919 2.283
beta0_black[5] 1.387 1.404 -0.681 1.334 3.454
beta0_black[6] 1.358 1.307 -0.875 1.342 3.346
beta0_black[7] 1.371 1.269 -1.112 1.340 3.522
beta0_black[8] 1.113 0.302 0.400 1.155 1.583
beta0_black[9] 1.660 0.504 0.697 1.628 2.574
beta0_black[10] 1.355 0.135 1.078 1.356 1.617
beta0_black[11] 3.303 0.312 2.441 3.353 3.700
beta0_black[12] 4.393 0.191 4.035 4.396 4.743
beta0_black[13] -0.095 0.253 -0.563 -0.089 0.342
beta0_black[14] 1.785 0.627 0.262 1.939 2.609
beta0_black[15] 0.984 0.410 -0.161 1.083 1.498
beta0_black[16] 3.353 0.998 0.645 3.668 4.361
beta2_black[1] 3.199 1.702 0.835 2.863 7.272
beta2_black[2] -1.661 2.324 -6.596 -1.324 3.345
beta2_black[3] 0.003 3.267 -6.195 0.087 6.510
beta2_black[4] -2.126 1.993 -7.070 -1.552 -0.062
beta2_black[5] 0.050 3.118 -5.903 0.012 6.298
beta2_black[6] -0.027 3.140 -6.167 0.045 6.030
beta2_black[7] 0.021 3.184 -6.283 0.048 6.146
beta2_black[8] -3.104 2.158 -7.534 -2.961 0.045
beta2_black[9] -1.428 2.469 -6.651 -1.062 4.009
beta2_black[10] -0.845 2.805 -6.122 -1.003 5.462
beta2_black[11] -1.637 1.957 -5.845 -1.437 2.893
beta2_black[12] -3.069 1.666 -7.230 -2.751 -0.787
beta2_black[13] -2.028 1.426 -5.927 -1.624 -0.420
beta2_black[14] -0.847 1.256 -4.760 -0.320 -0.079
beta2_black[15] -1.587 1.930 -6.205 -1.018 1.083
beta2_black[16] 1.816 2.195 -3.148 1.496 6.519
beta3_black[1] 41.841 0.792 40.265 41.942 43.023
beta3_black[2] 30.138 7.727 19.249 30.948 44.201
beta3_black[3] 27.626 7.101 19.190 27.683 44.294
beta3_black[4] 32.837 3.565 22.053 32.846 38.888
beta3_black[5] 31.806 7.378 19.771 31.607 45.006
beta3_black[6] 31.659 7.298 19.796 31.258 44.909
beta3_black[7] 31.695 7.396 19.812 31.325 45.009
beta3_black[8] 28.696 7.764 20.384 23.329 42.623
beta3_black[9] 34.438 8.410 19.654 35.476 44.958
beta3_black[10] 28.684 9.430 19.323 24.155 45.369
beta3_black[11] 33.650 4.170 29.114 32.288 44.334
beta3_black[12] 32.890 0.608 31.622 32.948 33.779
beta3_black[13] 39.277 0.720 37.687 39.363 40.472
beta3_black[14] 37.985 3.650 30.250 38.330 44.901
beta3_black[15] 36.248 5.117 29.257 35.491 45.424
beta3_black[16] 33.737 4.135 29.104 32.424 43.723
beta4_black[1] -0.269 0.190 -0.653 -0.266 0.089
beta4_black[2] 0.274 0.173 -0.058 0.275 0.606
beta4_black[3] -0.995 0.182 -1.360 -0.993 -0.635
beta4_black[4] 0.635 0.218 0.219 0.634 1.056
beta4_black[5] -0.013 3.190 -6.193 0.080 6.303
beta4_black[6] 0.048 3.180 -6.209 0.045 6.303
beta4_black[7] 0.024 3.181 -6.175 0.062 6.254
beta4_black[8] -0.834 0.371 -1.547 -0.841 -0.094
beta4_black[9] 2.104 1.089 0.245 2.008 4.527
beta4_black[10] 0.031 0.177 -0.319 0.032 0.381
beta4_black[11] -0.710 0.215 -1.140 -0.708 -0.301
beta4_black[12] 0.559 0.332 -0.068 0.553 1.203
beta4_black[13] -1.272 0.207 -1.671 -1.273 -0.863
beta4_black[14] -0.049 0.235 -0.508 -0.044 0.419
beta4_black[15] -0.946 0.211 -1.362 -0.945 -0.529
beta4_black[16] -0.602 0.229 -1.054 -0.597 -0.168
mu_beta0_black[1] 1.045 0.863 -0.998 1.106 2.460
mu_beta0_black[2] 1.316 0.581 0.044 1.333 2.300
mu_beta0_black[3] 1.948 1.175 -0.862 2.101 3.740
tau_beta0_black[1] 1.248 1.143 0.050 0.929 4.495
tau_beta0_black[2] 20.607 37.444 0.103 6.255 133.343
tau_beta0_black[3] 0.316 0.224 0.027 0.273 0.857
sigma_H[1] 0.212 0.054 0.121 0.206 0.329
sigma_H[2] 0.175 0.029 0.122 0.174 0.238
sigma_H[3] 0.187 0.042 0.109 0.184 0.275
sigma_H[4] 0.422 0.077 0.297 0.413 0.601
sigma_H[5] 1.038 0.215 0.647 1.032 1.474
sigma_H[6] 0.402 0.197 0.044 0.396 0.809
sigma_H[7] 0.299 0.059 0.208 0.291 0.435
sigma_H[8] 0.416 0.081 0.282 0.407 0.604
sigma_H[9] 0.524 0.123 0.335 0.509 0.811
sigma_H[10] 0.208 0.043 0.134 0.204 0.299
sigma_H[11] 0.275 0.046 0.198 0.270 0.379
sigma_H[12] 0.445 0.166 0.210 0.428 0.790
sigma_H[13] 0.217 0.037 0.153 0.214 0.297
sigma_H[14] 0.511 0.094 0.345 0.505 0.707
sigma_H[15] 0.247 0.041 0.181 0.242 0.337
sigma_H[16] 0.214 0.041 0.145 0.210 0.305
lambda_H[1] 2.688 3.635 0.136 1.546 11.968
lambda_H[2] 8.076 7.703 0.725 5.798 28.888
lambda_H[3] 6.139 9.432 0.287 3.117 29.816
lambda_H[4] 0.006 0.004 0.001 0.005 0.017
lambda_H[5] 2.910 6.653 0.032 0.734 21.212
lambda_H[6] 7.729 14.963 0.009 0.952 53.996
lambda_H[7] 0.016 0.011 0.003 0.014 0.044
lambda_H[8] 8.546 10.304 0.151 5.023 40.128
lambda_H[9] 0.016 0.011 0.003 0.013 0.044
lambda_H[10] 0.290 0.479 0.033 0.195 1.038
lambda_H[11] 0.254 0.575 0.011 0.126 1.160
lambda_H[12] 4.913 6.930 0.203 2.669 23.409
lambda_H[13] 3.134 2.878 0.229 2.378 10.618
lambda_H[14] 3.698 4.706 0.240 2.161 18.050
lambda_H[15] 0.026 0.048 0.003 0.017 0.098
lambda_H[16] 0.722 1.148 0.042 0.383 3.177
mu_lambda_H[1] 4.344 1.904 1.253 4.128 8.571
mu_lambda_H[2] 3.791 1.980 0.580 3.586 8.127
mu_lambda_H[3] 3.542 1.873 0.753 3.317 7.766
sigma_lambda_H[1] 8.647 4.299 2.055 8.061 18.122
sigma_lambda_H[2] 8.200 4.679 0.905 7.572 18.319
sigma_lambda_H[3] 6.396 4.091 1.028 5.559 16.502
beta_H[1,1] 6.861 1.117 4.215 7.020 8.609
beta_H[2,1] 9.883 0.485 8.814 9.913 10.794
beta_H[3,1] 7.980 0.788 6.157 8.075 9.264
beta_H[4,1] 9.270 7.650 -6.286 9.400 23.937
beta_H[5,1] 0.036 2.587 -5.436 0.258 4.660
beta_H[6,1] 3.132 3.989 -7.148 4.536 7.644
beta_H[7,1] 2.230 5.326 -9.195 2.627 11.632
beta_H[8,1] 1.177 2.776 -2.151 1.230 3.391
beta_H[9,1] 13.278 5.583 1.778 13.221 24.871
beta_H[10,1] 7.088 1.782 3.516 7.144 10.589
beta_H[11,1] 5.208 3.338 -2.245 5.837 9.904
beta_H[12,1] 2.629 1.075 0.754 2.553 4.995
beta_H[13,1] 9.053 0.974 6.995 9.133 10.564
beta_H[14,1] 2.185 1.001 0.188 2.194 4.217
beta_H[15,1] -5.966 3.805 -12.647 -6.199 2.418
beta_H[16,1] 3.420 2.606 -0.956 3.137 9.398
beta_H[1,2] 7.906 0.248 7.428 7.908 8.391
beta_H[2,2] 10.034 0.137 9.764 10.036 10.307
beta_H[3,2] 8.966 0.191 8.599 8.965 9.339
beta_H[4,2] 3.535 1.446 0.766 3.514 6.372
beta_H[5,2] 1.980 0.957 0.105 1.981 3.808
beta_H[6,2] 5.775 1.077 3.172 5.974 7.408
beta_H[7,2] 2.132 1.040 0.268 2.064 4.379
beta_H[8,2] 3.034 0.883 1.589 3.118 4.238
beta_H[9,2] 3.399 1.101 1.321 3.366 5.583
beta_H[10,2] 8.188 0.352 7.435 8.201 8.839
beta_H[11,2] 9.719 0.601 8.851 9.619 11.074
beta_H[12,2] 3.960 0.369 3.263 3.956 4.716
beta_H[13,2] 9.122 0.260 8.669 9.111 9.674
beta_H[14,2] 4.005 0.352 3.303 4.001 4.717
beta_H[15,2] 11.355 0.685 9.964 11.380 12.605
beta_H[16,2] 4.430 0.800 2.994 4.415 6.119
beta_H[1,3] 8.469 0.252 8.007 8.459 8.991
beta_H[2,3] 10.081 0.117 9.846 10.080 10.313
beta_H[3,3] 9.648 0.161 9.339 9.646 9.981
beta_H[4,3] -2.506 0.884 -4.247 -2.478 -0.822
beta_H[5,3] 3.995 0.641 2.710 4.005 5.239
beta_H[6,3] 8.103 1.212 6.447 7.746 10.777
beta_H[7,3] -2.213 0.686 -3.607 -2.186 -0.902
beta_H[8,3] 5.178 0.419 4.593 5.133 5.979
beta_H[9,3] -2.714 0.762 -4.268 -2.711 -1.281
beta_H[10,3] 8.728 0.278 8.173 8.722 9.256
beta_H[11,3] 8.572 0.280 7.984 8.595 9.063
beta_H[12,3] 5.259 0.323 4.494 5.299 5.770
beta_H[13,3] 8.808 0.182 8.424 8.810 9.156
beta_H[14,3] 5.674 0.272 5.076 5.691 6.169
beta_H[15,3] 10.392 0.317 9.776 10.391 11.001
beta_H[16,3] 6.053 0.551 4.910 6.087 7.018
beta_H[1,4] 8.222 0.191 7.803 8.234 8.559
beta_H[2,4] 10.134 0.119 9.871 10.140 10.353
beta_H[3,4] 10.128 0.163 9.771 10.139 10.419
beta_H[4,4] 11.757 0.449 10.869 11.758 12.640
beta_H[5,4] 5.717 0.812 4.378 5.629 7.544
beta_H[6,4] 7.144 0.924 4.968 7.411 8.377
beta_H[7,4] 8.154 0.336 7.487 8.157 8.813
beta_H[8,4] 6.679 0.223 6.272 6.684 7.089
beta_H[9,4] 7.193 0.478 6.209 7.193 8.135
beta_H[10,4] 7.732 0.230 7.296 7.728 8.211
beta_H[11,4] 9.282 0.209 8.870 9.285 9.670
beta_H[12,4] 7.155 0.215 6.752 7.148 7.610
beta_H[13,4] 8.990 0.145 8.692 8.990 9.270
beta_H[14,4] 7.658 0.212 7.242 7.654 8.085
beta_H[15,4] 9.431 0.240 8.951 9.433 9.897
beta_H[16,4] 9.229 0.218 8.829 9.224 9.692
beta_H[1,5] 8.986 0.152 8.677 8.989 9.276
beta_H[2,5] 10.789 0.096 10.607 10.787 10.985
beta_H[3,5] 10.928 0.162 10.638 10.920 11.249
beta_H[4,5] 8.381 0.469 7.478 8.365 9.311
beta_H[5,5] 5.361 0.640 3.857 5.421 6.427
beta_H[6,5] 8.783 0.601 7.935 8.658 10.226
beta_H[7,5] 6.798 0.327 6.167 6.794 7.445
beta_H[8,5] 8.198 0.203 7.840 8.187 8.595
beta_H[9,5] 8.192 0.473 7.269 8.199 9.114
beta_H[10,5] 10.104 0.224 9.666 10.103 10.535
beta_H[11,5] 11.558 0.231 11.097 11.561 11.985
beta_H[12,5] 8.513 0.206 8.108 8.511 8.928
beta_H[13,5] 10.011 0.134 9.740 10.010 10.281
beta_H[14,5] 9.190 0.238 8.763 9.177 9.682
beta_H[15,5] 11.205 0.243 10.726 11.203 11.681
beta_H[16,5] 9.932 0.170 9.576 9.937 10.252
beta_H[1,6] 10.244 0.205 9.894 10.227 10.705
beta_H[2,6] 11.525 0.107 11.319 11.526 11.734
beta_H[3,6] 10.823 0.161 10.478 10.836 11.108
beta_H[4,6] 12.878 0.812 11.272 12.878 14.466
beta_H[5,6] 5.868 0.640 4.634 5.851 7.127
beta_H[6,6] 8.787 0.660 7.089 8.894 9.717
beta_H[7,6] 9.766 0.553 8.687 9.764 10.859
beta_H[8,6] 9.530 0.262 9.049 9.537 9.966
beta_H[9,6] 8.459 0.802 6.888 8.437 10.077
beta_H[10,6] 9.519 0.310 8.852 9.545 10.054
beta_H[11,6] 10.815 0.353 10.059 10.844 11.460
beta_H[12,6] 9.403 0.260 8.904 9.393 9.975
beta_H[13,6] 11.085 0.170 10.786 11.077 11.446
beta_H[14,6] 9.893 0.292 9.304 9.901 10.457
beta_H[15,6] 10.841 0.421 10.016 10.846 11.686
beta_H[16,6] 10.478 0.251 9.926 10.494 10.906
beta_H[1,7] 10.868 0.952 8.528 10.999 12.363
beta_H[2,7] 12.226 0.441 11.329 12.233 13.068
beta_H[3,7] 10.573 0.659 9.181 10.624 11.721
beta_H[4,7] 2.430 4.141 -5.622 2.428 10.604
beta_H[5,7] 6.424 2.004 2.852 6.328 10.697
beta_H[6,7] 9.483 2.446 4.688 9.489 15.603
beta_H[7,7] 10.840 2.753 5.292 10.877 16.301
beta_H[8,7] 10.955 0.874 9.498 10.927 12.528
beta_H[9,7] 4.536 4.134 -3.843 4.547 12.550
beta_H[10,7] 9.910 1.422 7.392 9.822 13.003
beta_H[11,7] 11.068 1.711 7.949 10.952 14.688
beta_H[12,7] 10.010 0.956 7.942 10.097 11.591
beta_H[13,7] 11.679 0.806 9.790 11.772 12.904
beta_H[14,7] 10.523 0.943 8.500 10.598 12.158
beta_H[15,7] 12.289 2.215 7.816 12.262 16.563
beta_H[16,7] 12.451 1.355 10.306 12.266 15.576
beta0_H[1] 9.051 13.371 -18.089 8.925 36.726
beta0_H[2] 10.725 6.508 -3.112 10.741 23.626
beta0_H[3] 9.790 10.702 -11.536 9.939 29.855
beta0_H[4] 6.909 177.731 -342.866 10.565 358.548
beta0_H[5] 4.210 28.890 -49.581 4.014 60.150
beta0_H[6] 7.923 49.418 -96.655 7.588 119.656
beta0_H[7] 5.846 117.407 -234.855 9.308 240.730
beta0_H[8] 6.205 37.783 -14.022 6.552 25.070
beta0_H[9] 3.408 119.453 -248.555 2.228 246.674
beta0_H[10] 9.787 33.634 -56.464 9.842 77.999
beta0_H[11] 10.335 49.737 -90.614 9.649 113.983
beta0_H[12] 6.701 12.085 -16.855 6.972 29.739
beta0_H[13] 9.737 11.935 -12.648 10.013 32.942
beta0_H[14] 7.310 11.167 -14.829 6.973 30.102
beta0_H[15] 7.778 110.078 -206.455 8.368 234.407
beta0_H[16] 8.816 25.029 -41.889 8.552 62.032